Noticed a lack of haters on your Instagram feed lately? This may be thanks to the photo-sharing platform’s new filter, which uses machine learning to hide offensive comments. The potential opportunities of artificial intelligence systems, such as those implemented by Instagram, are drawing considerable attention from the business world. While this cutting-edge technology can’t be used in all situations, companies have every reason to be excited – applied in the right areas, machine learning will improve their top and bottom lines. Read the first part of this two-part series on machine learning.
Making a large number of pricing decisions in a short span of time can be absolutely crucial in a number of industries. The eCommerce sector is a prime example: many online retailers have tens of thousands of stock keeping units to manage and often sell their products in multiple markets with changing prices and demands, so the ability to react quickly is essential. The importance of price agility can also be seen at gas stations, which are known to change their prices numerous times daily.
Meeting the challenges of a dynamic market is not a simple feat. Processes can be automated using commercially available pricing software, but this requires specialists to define and adjust sets of fixed rules. While current software solutions continue to apply their rules uniformly until someone changes the model manually, machine learning will dramatically reduce this need for human interference. By analyzing past experiences, the machine will identify patterns, adapt pricing algorithms to reflect changes in the retail environment, and find relationships within data, ensuring manual input from pricing managers can be kept to a minimum.
Taking the guesswork out of determining discounts
Sales teams are always under pressure to achieve the best possible price when preparing a quote or negotiating with a customer. Until now, the most effective approach has been to segment customers according to personal characteristics, product attributes, and the specifics of the deal. As segmentation often relies on subjective observations, there are limits to the impact existing discount structures can have. This element of guesswork can lead to unnecessarily high discounts being granted.
The big advantage of machine learning systems is that they are able to identify every single piece of relevant information and apply it to fine-tune customer discounts. Data-driven decisions, rather than ones based on intuition, will help sales agents avoid making lowball offers, reducing profit leakage for the company as a result. With access to relevant historic conversion-rate data, machine-learning systems will even be able to predict whether a deal will be won or lost.
Ensuring the success of targeted promotions
How do we decide which customers should be targeted with promotions and which products should be included? Designing a successful price promotion is always a challenge, particularly when the data available is limited to sales figures and demographic information. In practice, companies often fall back on rule-of-thumb approaches to make these decisions, leading to ineffective price promotions that don’t maximize customers’ willingness to pay.
Machine learning systems will be able to process structured data (and ideally, even unstructured data, such as comments, pictures, website click journeys, etc.) to identify the most valuable target customers. Not only will this make promotions more effective, companies will also be able to develop sophisticated price discrimination processes that offer customers discounts personalized to their specific situation and generate additional revenues.
Opportunities and pitfalls
The potential applications for machine learning in pricing don’t stop here. Self-improving algorithms could also be used to automate lead scoring, calculate price elasticity, predict customer choice, and determine churn rates, just to name a few. However, the myriad of possibilities in this new field of technology brings with it its own set of risks. Read the concluding part of this series, which discusses the pitfalls you need to be aware of and some of the ways you should prepare before implementing machine-learning solutions within your business.
Machine Learning: Avoiding Data-Driven Disasters - read part 2 of the series here